Functional specialisation and economic upgrading in GVCs - Open Research Data - Bridge of Knowledge

Search

Functional specialisation and economic upgrading in GVCs

Description

The dataset collected for selected Central Eastern European (CEE) countries (CZE, EST, HUN, LVA, LTU, POL, SVK, SVN) contains country-level and sector-level observations for the project implementation linked to the concept of functional specialization of economies.
The aim of the project is to identify patterns of functional specialisation in global value chains (GVC) and determinants of upgrading it for selected CEE countries.
Functional specialisation index (rca_fsi) which provides a basis for the construction of dependent binary variable (fsi) in analysed models is obtained with the aid of an online appendix with replication files and the Matlab codes provided by Timmer et al. (2019).
The dataset contains Stata format files.
The results confirm that the higher labour productivity, labour compensation, skills and backward linkages are, the greater the probability is of climbing up the smile curve in management tasks and R&D activities. Upgrading functional specialisation in marketing tasks is supported by strong GVC linkages and may occur when wages are rising.
Marcel P Timmer, Sébastien Miroudot, Gaaitzen J de Vries, Functional specialisation in trade [dataset/supplementary data], Journal of Economic Geography, Volume 19, Issue 1, January 2019, Pages 1–30, https://doi.org/10.1093/jeg/lby056

Dataset file

data_functional specialisation_CEE.zip
234.6 kB, S3 ETag 676eedeb9eb544ba9f570be25d0f4394-1, downloads: 91
The file hash is calculated from the formula
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.

Example script for calculation:
https://github.com/antespi/s3md5
download file data_functional specialisation_CEE.zip

File details

License:
Creative Commons: by-nc 4.0 open in new tab
CC BY-NC
Non-commercial

Details

Year of publication:
2020
Verification date:
2020-12-17
Creation date:
2020
Dataset language:
English
Fields of science:
  • economics and finance (Social studies)
DOI:
DOI ID 10.34808/hzrt-aq62 open in new tab
Verified by:
Gdańsk University of Technology

Keywords

Cite as

seen 253 times